In a device having such an architecture several processors operate simultaneously on different data by means of a single command and several results are output (this mode is referred to as the Singe Instruction/Multiple Data or SIMD mode). Extensively parallel architectures of this kind are used notably in neural networks. EP 0 322 966 describes a neural network whose architecture can be broken down into several elementary processors, each of which comprises a memory section for synaptic coefficients C.sub.ij, a register for neuron states V.sub.i, and means for calculating contributions C.sub.ij.V.sub.i. All these elementary processors share a common input bus and output in parallel a plurality of contributions C.sub.ij. V.sub.i which are added in an adder tree.
Devices of this kind are conceived to ensure that the data follows a direct path (stream) between the input and the output. This type of architecture is provided so as to increase the execution speed of such data processing devices. Therefore, they are conceived to control at a maximum speed the data streams following parallel paths from the input to the output of said device. While following said parallel paths, the data streams remain independent until the instant at which they are collected so as to be collectively added, compared or otherwise.
Neural networks notably implement operations which become more and more sophisticated as applications become more and more diversified, utilizing more and more complex algorithms. For these diversified applications, a neural network having a given hardware structure must have available a wide range of instructions, enabling the network to implement a maximum number of applications and hence algorithms dedicated to each of these applications. Notably data streams following exclusively parallel paths do not allow for the processing of interactions between these streams in the course of their thus far independent processing.
In a neural network, these streams may be regrouped at the end of processing, but may not be combined before such final regrouping. Such a neural network thus lacks flexibility for adaptation to diversified tasks.